“Television as a service” provides value to both operators and end users alike. For the end users, it provides entertainment value and for operators, it provides a platform to advertise shows, movies, etc., to those end users. With the advent of the modern, on-demand lifestyle largely enabled by the widespread adoption of the Internet and its penetration into the everyday lives of many people, being able to provide users with personalized content has become key.
There are many ways to promote and personalize content to people. One example used in some environments, and particularly in television as a service, includes using hand-curated lists of recommended content. Another common, more automated solution includes using statistical techniques such as machine learning to generate content recommendations for users based on, for example, the user's indicated or deduced preferences and/or past consumption history.
Within the realm of providing audiovisual content, video-on-demand and live television are two predominant forms. With video-on-demand (or “VOD”), it is relatively easy to curate a list of recommendations or generate personalized recommendations automatically. For example, once there is data gathered on a given user, anything in the VOD store is available to be advertised. The larger the VOD store, the more likely a user will identify with the recommended content and view it. As content in these VOD stores is typically present for a set amount of time, curated lists can be properly managed in a timely manner.
However, systems utilizing live television do not have the convenience of a “static” store of content from which to pull recommended items from, or from which to pull media content metadata from. Thus, operators may choose to use “trending” algorithms to highlight those programs that are being heavily viewed, or simply present a complete list of all such live content being provided at a particular point in time from multiple content providers (e.g., display a menu of all currently and/or upcoming content items from multiple “channels” of content) or a list of upcoming content items from a particular provider (e.g., an indicator of a content item that will be on after a currently-airing program).
Accordingly, although VOD curation and automatic recommendation solutions do not have many downfalls, “live” television recommendation systems have many. As mentioned above, live television systems do not have a static store of content, as live television is always changing and new pieces of content can possible begin and end every minute. Additionally, attempting to generate manually-curated lists of recommended items for content that is currently airing is near impossible to maintain, and cannot effectively be customized on a per-user basis.
Further, in systems employing live television the trending algorithms are not very accurate, as trending algorithms can show only what is popular across the entire channel map, and thus does not take into account what types of content the user prefers or normally watches.
Moreover, recommendations of upcoming content (e.g., a “next” TV program) cannot be immediately viewed by users. Thus, feeds that advertise shows that are upcoming do not provide any immediate value to users who wish to view content at that moment.
Accordingly, there remains a strong need for personalized recommendation systems for live television content.